Fusion of Multi-Model Biometric Authentication Systems Using Iris and Palmprint

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1 ISSN (e): Volume, 07 Issue, 08 August 2017 Internatonal Journal of Computatonal Engneerng Research (IJCER) Fuson of Mult-Model Bometrc Authentcaton Systems Usng and Palmprnt 1 P SRINIVAS, 2 Dr. K. Rama Krshna Reddy, 3 Ms. Y.L. Malathlatha 1 Assocate professor, Malla Reddy Engneerng College Autonomous, Masammagud, Dhlaally, Kompally, Dst, Medchal, Telagana State. 2 Assocate Professor, Malla Reddy Engneerng College Autonomous Masammaguda, Dhlapally, Kompally, Dst, Medchal, Telagana State, Inda. 3 Assocate profeoessor, Swam Vvekanada Insttute of Technology, Secunderabad, Telagana State. Correspondng Author: P SRINIVAS ABSTRACT Ths paper presents fuson of two bometrc trats,.e., rs and palmprnt, at matchng score level archtecture usng weghted sum of score technque. The features are extracted from the pre-processed mages of rs and palmprnt. These features of a query mage are compared wth those of a template database mage to obtan matchng scores. The ndvdual scores generated after matchng are passed to the fuson module. Ths module conssts of three major steps.e., normalzaton, generaton of smlarty score and fuson of weghted scores. The fnal score s then used to declare the person as genune or an mpostor. The system s tested on template databases collected by the authors for 1250 samples and gves an overall accuracy of 97.04% wth FAR of 0.58% and FRR of 4.34%. Keywords:, Palmprnt, Genune, Impostor, Sum rule and Fuson Date of Submsson: Date of acceptance: I. INTRODUCTION Bometrcs refers to the use of physologcal or bologcal characterstcs to measure the dentty of an ndvdual. These features are unque to each ndvdual and reman unaltered durng a person s lfetme. These features make bometrcs a promsng soluton to the socety. The access to the secured area can be made by the use of ID numbers or password whch amounts to knowledge based securty. But such nformaton can easly be accessed by ntruders and they can breach the doors of securty. The problem arses n case of monetary transactons and hghly restrcted to nformaton zone. Thus to overcome the above mentoned ssue bometrc trats are used. A bometrc system s essentally a pattern recognton system whch makes a personal dentfcaton by determnng the authentcty of a specfc physologcal or behavoral characterstc possessed by the user. Bometrc technologes are thus defned as the automated methods of dentfyng or authentcatng the dentty of a lvng person based on a physologcal or behavoral characterstc. The varous bometrcs trats avalable are face, fngerprnt, rs, palmprnt, hand geometry, ear etc. Among the avalable bometrc trats some of the trats outperform others. The relablty of several bometrcs trats s measured wth the help of expermental results. The bometrc system s bascally dvded nto two modes.e., unmodal bometrc system and multmodal bometrc system. In case of unmodal bometrc system the ndvdual trat s used for recognton or dentfcaton. The system performs better under certan assumptons but fals when the bometrc data avalable s nosy. The system also fals n case of unavalablty of bometrc template. Thus n such a stuaton multmodal bometrc systems are used where more than one classfer s used to arrve at a fnal decson. The concept of multmodal bometrc system has been proposed by Ross and Jan [1] where apart from fuson strateges varous levels of ntegraton are also presented. In [2] fuson of rs and face bometrcs has been proposed. The score level fuson n multmodal bometrcs system s proposed n [3]. A novel fuson at feature level for face and palmprnt has been presented n [4]. The purpose n [4] s to nvestgate whether the ntegraton of face and palmprnt bometrcs can acheve hgher performance that may not be possble usng a sngle bometrc ndcator alone. Both Prncpal Component Analyss (PCA) and Independent Component Analyss (ICA) are consdered n ths feature vector fuson context. It s found that the performance has mproved sgnfcantly. In ths paper a novel combnaton of rs and palmrprnt bometrcs s presented. The two classfers perform better ndvdually but fal under certan condtons. The problem can arse at the tme of rs mage acquston where the user has to be co-operatve whle gvng the rs mage. In case of palmprnt recognton poor qualty Open Access Journal Page 114

2 Fuson Of Mult-Model Bometrc Authentcaton Systems Usng And Palmprnt palmprnt mage may create problem. The enhancement module recovers the rdges and wrnkles present but the loss due to cuts and scars present on the palmprnt mage may create problem n extracton of mnutae ponts dstance. Thus the two recognzers are combned at matchng score level and fnal decson about the person s dentty s made. In the next secton a bref overvew s presented about the rs and palmprnt. In Secton 3 the two modaltes are combned at matchng score level. The expermental results pror to fuson and after fuson are presented n Secton 4. Concluson s gven n the last secton. II. INDIVIDUAL RECOGNIZERS and Palmprnt bometrcs perform better as compared to other avalable trats due to ther accuracy, relablty and smplcty. These propertes make rs and palmprnt recognton partcularly promsng soluton to the socety. The process starts wth preprocessng of the acqured mages whch removes the effect of nose. Further, features are extracted for the tranng and testng mages and matched to fnd the smlarty between two feature sets. The matchng scores generated from the ndvdual recognzers are passed to the decson module where a person s declared as genune or an mposter. 2.1 Recognton s unque to each ndvdual and remans constant over the lfe of a person. The eyeball has a crcular black dsk n the center known as pupl. The pupl dlates when exposed to lght and contracts n dark. Thus the sze of pupl vares wth respect to lght t s exposed to. The rs s the annular rng between the sclera and pupl boundary and contans the flowery pattern unque to each ndvdual. Ths texture nformaton unque to each ndvdual s extracted from rest of the eye mage and s transformed nto strp to apply pattern matchng algorthm between the database and query mages of rs. Automated rs recognton system has been proposed by Flom and Safr [5]. The concept of mult-scale quadrature wavelets s used to extract texture phase structure nformaton of the rs, to generate a 2048 bt rs code and compares the dfference between a par of rs representatons by computng ther Hammng dstance usng XOR operator [6]. The zero-crossng representaton of 1-D wavelet transform at varous resoluton levels of a vrtual crcle on an rs mage to characterze the texture of the rs has been calculated n [7]. Wldes et al. [8] obtan the rs texture wth a Laplacan pyramd constructed wth four dfferent resoluton levels and use the normalzed correlaton to determne whether the nput mage and the model mage are from the same class. The mportant steps nvolved n rs recognton are: 1. Pupl Detecton 2. Detecton 3. Normalzaton 4. Feature Extracton 5. Matchng Pror to mplementaton of steps mentoned above rs mage has to be acqured whch should be rch n texture because all subsequent stages depend upon the mage qualty. The mages are acqured usng a 3CCD camera under the controlled lab envronment. The acqured mage s passed to the localzaton module to detect rs porton from the rest of the mage Pupl Detecton Pupl s the darkest porton of the eye and s detected and removed from the rest of the eye mage so that only rs pattern can be used for matchng. The frst step nvolved n pupl detecton s to fnd the contours of the acqured rs mage. Snce the pupl regon contans the lowest ntensty values ts edges can be formed easly. After edge detecton the next step s to fnd the center of the pupl. Thus the process starts wth dlatng the edge detected mage and the dlated mage wth flled pupl crcle s used to fnd the Eucldean dstance between the non-zero ponts. By computng the dstance between non-zero ponts the spectrum showng the largest flled crcle can be formed wthn the set of pxels. Snce the pupl s the largest flled crcle n the mage the overall ntensty of the spectrum peaks n at the center. Ths spectrum mage can be used to compute the center of the pupl. The pxel poston havng the maxmum value n the spectrum mage corresponds to the pupl center. The radus of the pupl s the dstance between the pupl center and nearest non-zero pxel Detecton To fnd the outer rs boundary ntensty varaton approach s used. In ths approach concentrc crcles of dfferent rad are drawn from the detected center. The crcle havng maxmum change n ntensty wth respect to prevous drawn crcle s rs crcle. The approach works fne for rs mages havng sharp varaton between rs boundary and sclera. The radus of rs and pupl boundary s used to transform the annular porton to a rectangular block, known as strp. Open Access Journal Page 115

3 Fuson Of Mult-Model Bometrc Authentcaton Systems Usng And Palmprnt Normalzaton The localzed rs mage s transformed nto strp. The mappng s done after transformng the Cartesan coordnates nto ts polar equvalent usng I ( x (, ), y (, )) wth x (, ) x x (, ) x p y (, ) y y (, ) x p 0 0 ( ) r ( ) r ( ) r * cos( ) ( ) r p I (, ) * cos( ) where r p and r are respectvely the radus of pupl and the rs n equaton (1), whle (x p (θ), y p (θ)) and (x (θ), y (θ)) are the coordnates of the pupllary and lmbc boundares n the drecton θ. The value of θ belongs to [0; 2 ], ρ belongs to [0; 1]. The transformed rs mage conssts of ponts taken from the pupl boundary to the outer rs boundary. Thus the same set of ponts s taken for every mage. The rs mage s normalzed so that the sze of strp does not vary for dfferent mages. The sze of same rs mage may vary due to expanson and dlaton of pupl. Thus the sze of rs strp s fxed for every rs mage. In ths experment the sze of strp s pxels Feature Extracton Features are the attrbutes or values extracted to get the unque characterstcs from the mage. Features from the rs mage are extracted usng Haar Wavelet decomposton process [9]. In the wavelet decomposton the mage s decomposed nto four coeffcent.e., horzontal, dagonal, vertcal and approxmaton. The approxmaton coeffcents are further decomposed nto four coeffcents. The sequences of steps are repeated for fve levels and the last level coeffcents are combned to form a vector. The combned vector s bnarzed to allow easy comparsons between the rs codes for template database and query mage. 1 IC ( ) p * sn( * sn( The bnarzed feature vectors are passed to the matchng module to allow comparsons. ) FV ( ) FV ( ) ) (1) (2) Matchng The comparson s done between rs codes (IC) generated for template database and query mages usng hammng dstance approach. In ths approach the dfference between the bts of two codes are counted and the number s dvded by the total number of comparsons. N 1 MS A B (3) N 1 where A s the bnary vector (rs code) for database mage and B s the bnary vector for query mage whle N s the number of elements[14]. Ths matchng score (MS ) s used as nput for the fuson module where the fnal matchng score s generated. 2.2 Palmprnt Recognton Palmprnt s one of the most wdely used bometrc modalty. The man reason behnd the use of palmprnt bometrc s that t s the most proven technque to dentfy the ndvdual. The palmprnt s bascally the combnaton of rdges and wrnkles on the surface of the rdges. The systematc study on the rdge, furrow, and pore structure n palmprnts has been publshed n [10]. The use of mnutae ponts feature for sngle fngerprnt classfcaton has been ntroduced n [10]. A system on palmprnt classfcaton s dscussed n [10] [11]. The major steps nvolved n palmprnt recognton usng mnutae matchng approach after mage acquston are: 1. Image Enhancement 2. Mnutae Extracton 3. Matchng Image Enhancement A palmprnt mage s corrupted due to varous knds of noses such as creases, smudges and holes. It s almost mpossble to recover the true rdge/wrnkle structures from the unrecoverable regons; any effort to mprove the qualty of the palmprnt mage n these regons may be futle. Therefore, any well known enhancement algorthm may be used to mprove the clarty of rdges/wrnkles structures of palmprnt mages n recoverable Open Access Journal Page 116

4 Fuson Of Mult-Model Bometrc Authentcaton Systems Usng And Palmprnt regons and to mask out the unrecoverable regons. The steps nvolved n mage enhancement are gven n [12]. The process starts wth normalzaton of nput palmprnt mage so that t has pre-specfed mean and varance. The orentaton mage s estmated from the normalzed nput palmprnt mage. Further, frequency mage s computed from the normalzed nput palmprnt mage and the estmated orentaton mage. After computaton of frequency mage the regon mask s obtaned by classfyng each block n the normalzed nput palmprnt mage nto a recoverable or unrecoverable block. Lastly, a bank of Gabor flters whch s tuned to local rdge orentaton and rdge frequency s appled to the rdge and valley pxels n the normalzed nput palmprnt mage to obtan an enhanced palmprnt mage Mnutae Extracton The enhanced palmprnt mage s bnarzed and submtted to the thnnng algorthm whch reduces the rdge thckness to one pxel wde. The skeleton mage s used to extract mnutae ponts whch are the ponts of rdge endngs and bfurcatons. The locaton of mnutae ponts along wth the orentaton s extracted and stored to form feature set. For extracton of mnutae ponts eght connected pxels are used [13]. The Crossng Number (CN) method s used to perform mnutae extracton. Ths method extracts the rdge endngs and bfurcatons from the skeleton mage by examnng the local neghborhood of each rdge pxel usng a 3 3 wndow. The CN for a rdge pxel P s gven by CN P P P 9 = P 1 where P s the pxel value n the neghborhood of P. After the CN for a rdge pxel has been computed, the pxel can then be classfed accordng to ts CN value. A rdge pxel wth a CN of one corresponds to a rdge endng, and a CN of three corresponds to a bfurcaton. For each extracted mnutae pont, the followng nformaton s recorded: x and y coordnates, orentaton of the assocated rdge segment, and type of mnutae (rdge endng or bfurcaton) Matchng The template database and query palmprnts are used for mnutae extracton and stored as ponts n the two dmensonal plane. A mnuta based matchng essentally conssts of fndng algnment between the template and the nput mnutae sets that results n the maxmum number of mnutae parngs. Let A = {m 1 m m } and B = {m 1.m n } be the set of mnutae ponts extracted from database and query mages respectvely. Where m={x,y,θ}, x and y are the coordnates at partcular mnutae pont and θ s the orentaton. The two sets are pared usng sd dd mn( 2 1 ( x x ) ( y y ) r j,360 j j j 2 0 ) Mnutae n A and a mnuta B are consdered to be matched f the spatal dstance (sd) between them s smaller than a gven tolerance r 0 and the drecton dfference (dd) between them s smaller than an angular tolerance θ 0. The parng generates a smlarty score (MS Palmprnt ) whch s passed to decson module. III. FUSION No ndvdual trat can provde 100% accuracy. Further, the results generated from the ndvdual trats are good but the problem arses when the user s not able to gve hs rs mage due to problem n exposure to lght. As eye s the most senstve organ of human body, the problem becomes severe when there exst some eye dseases. Thus n such a stuaton an ndvdual cannot be recognzed usng the rs patterns and the bometrc system comes to a standstll. Smlarly, the problem faced by palmprnt recognton system s the presence of scars and cuts. The scars add noses to the palmprnt mage whch cannot be enhanced fully usng enhancement module. Thus, the system takes nosy palmprnt as nput whch s not able to extract the mnutae ponts correctly and n turn, leads to false recognton of an ndvdual. Thus to overcome the problems faced by ndvdual trats of rs and palmprnt, a novel combnaton s proposed for the recognton system. The ntegrated system also provde ant spoofng measures by makng t dffcult for an ntruder to spoof multple bometrc trats smultaneously. Scores generated from ndvdual trats are combned at matchng score level usng weghted sum of score technque. Let MS and MS Fnger be the matchng scores obtaned from rs and palmprnt modaltes respectvely. The steps nvolved are: 3.1 Score Normalzaton Ths step brngs both matchng scores between 0 and 1 [14]. The normalzaton of both the scores are done by 0 (4) (5) Open Access Journal Page 117

5 Fuson Of Mult-Model Bometrc Authentcaton Systems Usng And Palmprnt MS mn N max mn (6) MS mn palmpr nt Palmprnt N Palmpr nt max mn Palmpr nt Palmpr nt where mn and max are the mnmum and maxmum scores for rs recognton and mn Palmprnt and max palmprnt are the correspondng values obtaned from palmprnt trat. 3.2 Generaton of Smlarty Scores Note that the normalzed score of rs whch s obtaned through Haar Wavelet gves the nformaton of dssmlarty between the feature vectors of two gven mages whle the normalzed score from palmprnt gves a smlarty measure. So to fuse both the score, there s a need to make both the scores as ether smlarty or dssmlarty measure. In ths paper, the normalzed score of rs s converted to smlarty measure by N 1 N (7) 3.3 Fuson The two normalzed smlarty scores N and N palmprnt are fused lnearly usng sum rule as MS * N * N Palmpr nt (8) where α and β are two weght values that can be determned usng some functon. In ths paper a combnaton of lnear and exponental functon s used. The value of weght s assgned lnearly f the value of matchng score s less than the threshold; otherwse exponental weght age s gven to the score. The value of MS s used as the matchng score. So f MS s found to be more than the gven threshold value the canddate s accepted otherwse t s rejected. IV. EXPERIMENTAL RESULTS The results are tested on rs and palmprnt mages collected by the authors. The database conssts of three rs mages (250 3) and two palmprnt mages (250 2) per person wth total of 250 persons. The rs mages are acqured usng CCD camera wth unform lght source. However, palmprnt mages are acqured usng an optcal palmprnt scanner. For the purpose allowng comparsons two levels of experments are performed. At frst level rs and palmprnt algorthms are tested ndvdually. At ths level the ndvdual results are computed and an accuracy curve s plotted as shown n Fgure 1. At ths level the ndvdual accuracy for rs and palmprnt s found to be 94.36% and 92.06% respectvely as shown n Table 1. However n order to ncrease the accuracy of the bometrc system as a whole the ndvdual results are combned at matchng score level. At second level of experment the matchng scores from the ndvdual trats are combned and fnal accuracy graph s plotted as shown n Fgure 2. Table 1 shows the accuracy and error rates obtaned from the ndvdual and combned system. The overall performance of the system has ncreased showng an accuracy of 97.04% wth FAR of 0.58% and FRR of 4.34% respectvely. Recever Operatng Characterstc (ROC) curve s plotted for Genune Acceptance Rate (GAR) aganst False Acceptance Rate (FAR) for ndvdual recognzers and combned system as shown n Fgure 3. From the plot t s clear that ntegrated system s gvng hghest GAR at lowest FAR. The Hstograms for genune and mposter data are shown n Fgure 4 below. The dstrbuton of genune and mposter data shows that at threshold of 0.5 the system would gve mnmum FAR and FRR rates wth maxmum accuracy of 97.04%. Fgure 1. Accuracy plots of ndvdual recognzers Open Access Journal Page 118

6 Accuracy Fuson Of Mult-Model Bometrc Authentcaton Systems Usng And Palmprnt Threshold Fgure 2. Accuracy graph for combned classfer Table 1. Fgures showng ndvdual and combned accuracy Trat Algorthm Accuracy FAR FRR Haar Wavelet Palmprnt Gabor Matchng Fuson Haar Mnutae Fgure 3. ROC Curve for Palmprnt, and Fuson Open Access Journal Page 119

7 Fuson Of Mult-Model Bometrc Authentcaton Systems Usng And Palmprnt Fgure 4. Hstogram for (a) genune and (b) mposter scores V. CONCLUSIONS The paper proposes a bometrc personal authentcaton system usng a novel combnaton of rs and palmprnt. For system deployment the combnaton s found to be useful as one needs a close up system and other needs contact. One modalty s used to overcome the lmtatons posed by the other. The expermental results show that the accuracy of system would ncrease on combnng the trats. The system s gvng an overall accuracy of 97.04% wth FAR and FRR of 0.58% and 4.34%. REFERENCES [1] A. Ross, & A. K. Jan, Informaton Fuson n Bometrcs, Pattern Recognton Letters, 24(13), 2003, [2] W. Yunhong, T. Tan, & A. K. Jan, Combnng Face and Bometrcs for Identty Verfcaton, Proceedngs of Fourth Internatonal Conference on AVBPA, Guldford, UK, 2003, [3] S. C. Dass, K. Nandakumar, & A. K. Jan, A Prncpled Approach to Score Level Fuson n Multmodal Bometrc Systems, Proc. of Audo- and Vdeo-based Bometrc Person Authentcaton (AVBPA), Rye Brook, NY, [4] G. Feng, K. Dong, D. Hu, & D. Zhang, When Faces Are Combned wth Palmprnts: A Novel Bometrc Fuson Strategy, Internatonal Conference on Bonformatcs and ts Applcatons, Hong Kong, Chna, 2004, [5] L. Flom, & A. Safr, Recognton System, U.S. Patent No , [6] J. G. Daugman, Hgh confdence vsual recognton of persons by a test of statstcal ndependence, IEEE Transactons on Pattern Analyss and Machne Intellgence, 15(11), 1993, [7] W. W. Boles, & B. Boashah, A Human Identfcaton Technque Usng Images of the and Wavelet Transform, IEEE Transacton on Sgnal Processng, 46(4), 1998, [8] R. Wldes, J. Asmuth, G. Green, S. Hsu, R. Kolczynsk, J. Matey, & S. McBrde, A Machne vson System for Recognton, Machne Vson and Applcatons, 9(1), 1996, 1-8. [9] A. E. Hassanen, & J M. Al, An Recognton System to Enhance E-securty Envronment Based on Wavelet Theory, Advanced Modelng and Optmzaton Journal, 5(2), 2003, [10] H. C. Lee, & R. E. Gaensslen, Eds., Advances n Palmprnt Technology (New York, Elsever, 1991). [11] Federal Bureau of Investgaton, The Scence of Palmprnts (Classfcaton and Uses) (Washngton, D.C., US Govt. Prntng Offce, 1984). [12] L. Hong, Y. Wan, & A.K. Jan, Palmprnt Image Enhancement: Algorthm and Performance Evaluaton, IEEE Transactons on Pattern Analyss and Machne Intellgence, 20(8), 1998, [13] Raymond Tha, Palmprnt Image Enhancement and Mnutae Extracton, Techncal Report, The Unversty of Western Australa, [14] A. K. Jan, K. Nandakumar, & A. Ross, Score Normalzaton n multmodal bometrc systems. The Journal of Pattern Recognton Socety, 38(12), 2005, Open Access Journal Page 120

8 Fuson Of Mult-Model Bometrc Authentcaton Systems Usng And Palmprnt Authors Prof. P.SRINIVAS, receved degrees n B.E.(CSE), n 1995 from Osmana Unversty, Hyderabad, M.Tech (CSE) n 2005 from Osmana Unversty, Hyderabad. Ph.D Scholar. He s currently workng as Professor n Malla Reddy Engneerng College(Autonomous) Masamma Guda, Dulapally, Kompally Sec-bad, Telangana State. Inda. Hs publshed fve papers n top level Internatonal Journals & Conferences (Sprnger s, Elsever& IEEE). Hs research area of nterests are Pattern Recogntons, Bometrcs, Data Base Management Systems, Web Technology, Web Servces, Image Processng, LP, C, C++, OOPS & JAVA. Hs 20 years teachng experence and 3 years experence n IT sector. Dr. K.RAMA KRISHNA REDDY, receved Ph.D degree (Computer Engneerng) n 2016 from Delh Technologcal Unversty,New Delh, Inda. I receved degrees n M.Tech (CSE) & B.Tech (CSE) from JNTUH, Hyderabad. Hs workng as Assoc. Professor n CSE department at Malla Reddy Engneerng College (Autonomous), Hyderabad. Hs research ncludes Management Informaton Systems, Computer Networks, Informaton Securty, Cloud Computng, Informaton Retreval Systems, Data Mnng, Pattern Recognton and Software Engneerng. Hs 10 years of teachng and research experence. Ms. Y. L Malath Latha, receved Master Degree n Computer Scence n Engneerng n 2008 from Osmana Unversty. Ph.D Scholar. She s currently workng as Assocate Professor n Swam Vvekananda Insttute of Technology (SVIT), Mahabub College, Campus, Pantny Center, Hyderabad, Telangana States, Inda. Her publshed seven papers n top level Internatonal Journals & Conferences (Sprnger s, IGI, Elsever and IEEE). Her publshed n IGI book chapter. Her research area nterests are Pattern Recogntons, Bometrcs, Data Base Management Systems, Web Technology, Informaton Securty, Image Processng, OOPS & JAVA. Her 15 years of teachng and 8 years of research experence. Open Access Journal Page 121